Web analytics is the measurement, collection, analysis, and reporting of web data for purposes of understanding and optimizing web usage. Web analytics can be used as a tool to assess and improve the effectiveness of a website. Web analytics may be off-site, on-site, or a combination of the two. Off-site web analytics refers to web measurement and analysis, regardless of whether a person owns a website, and includes the measurement of a website's potential audience, webpage loading time, data accessed, type of device used to access the site by a user, and potentially other information. On-site web analytics can be used by the owner of a website to measure a visitor's behavior on the website. On-site web analytics can include user interface (UI) widget element accesses, user inaction, user interactions on a webpage, inaction on a webpage (e.g., hovering a mouse), mouse clicks on a webpage, and potentially other information.
Web design encompasses many different skills and disciplines in the production and maintenance of websites. The different areas of web design include web graphic design; interface design; authoring, including standardized code and proprietary software; user experience design; and search engine optimization. Often, many individuals will work in teams covering different aspects of the design process. The term “web design” is normally used to describe the design process relating to the front-end (client side) design of a website including writing mark up. Web design partially overlaps web engineering in the broader scope of web development. Web designers are expected to have an awareness of usability and if their role involves creating mark up then they are also expected to be up to date with web accessibility guidelines.
In marketing, A/B testing is a simple randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. A/B testing is a form of statistical hypothesis testing. Other names for statistical hypothesis testing include randomized controlled experiments, online controlled experiments, and split testing. In online settings, such as web design (especially user experience design), the goal of A/B testing is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement). As the name implies, two versions (A and B) are compared, which are identical except for one variation that might affect a user's behavior. For example, version A might be the currently used version (control), while version B is modified in some respect (treatment). For instance, on an e-commerce website the purchase funnel is typically a good candidate for A/B testing, as even marginal improvements in drop-off rates can represent a significant gain in sales. Significant improvements can sometimes be seen through testing elements like copy text, layouts, images, and colors. The vastly larger group of statistics broadly referred to as multivariate or multinomial testing is similar to A/B testing, but may test more than two different versions at the same time and/or has more controls, etc. Simple A/B tests are not valid for observational, quasi-experimental or other non-experimental situations, as is common with survey data, offline data, and other, more complex phenomena.